dgx-spark-playbooks/nvidia/vscode

Install VS Code

Install and use VS Code locally or remotely on Spark

Table of Contents

• Overview
• Instructions
• Access with NVIDIA Sync

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Overview

Basic idea

This walkthrough establishes a local Visual Studio Code development environment directly on DGX Spark devices. By installing VS Code natively on the ARM64-based Spark system, you gain access to a full-featured IDE with extensions, integrated terminal, and Git integration while leveraging the specialized hardware for development and testing.

What you'll accomplish

You will have Visual Studio Code running natively on your DGX Spark device with access to the system's ARM64 architecture and GPU resources. This setup enables direct code development, debugging, and execution on the target hardware without remote development overhead.

What to know before starting

• Basic experience working with Visual Studio Code interface and features

• Familiarity with package management on Linux systems

• Understanding of file permissions and authentication on Linux

Prerequisites

• DGX Spark device with administrative privileges

• Active internet connection for downloading the VS Code installer

• Verify ARM64 architecture:

uname -m
#  # Expected output: aarch64

• Verify GUI desktop environment available:

echo $DISPLAY
#  # Should return display information like :0 or :10.0

Time & risk

• Duration: 10-15 minutes
• Risk level: Low - installation uses official packages with standard rollback
• Rollback: Standard package removal via system package manager
• DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU. With many applications still updating to take advantage of UMA, you may encounter memory issues even when within the memory capacity of DGX Spark. If that happens, manually flush the buffer cache with:

sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'

Instructions

Step 1. Verify system requirements

Before installing VS Code, confirm your DGX Spark system meets the requirements and has GUI support.

## Verify ARM64 architecture
uname -m

## Check available disk space (VS Code requires ~200MB)
df -h /

## Verify desktop environment is running
ps aux | grep -E "(gnome|kde|xfce)"

Step 2. Download VS Code ARM64 installer

Navigate to the VS Code download page and download the appropriate ARM64 .deb package for your system.

Alternatively, you can download the installer with this command:

wget https://code.visualstudio.com/sha/download?build=stable\&os=linux-deb-arm64 -O vscode-arm64.deb

Step 3. Install VS Code package

Install the downloaded package using the system package manager.

You can click on the installer file directly or use the command line.

## Install the downloaded .deb package
sudo dpkg -i vscode-arm64.deb

## Fix any dependency issues if they occur
sudo apt-get install -f

Step 4. Verify installation

Confirm the VS Code app is installed successfully and can launch.

You can open the app directly from the list of applications or use the command line.

## Check if VS Code is installed
which code

## Verify version
code --version

## Test launch (will open VS Code GUI)
code &

VS Code should launch and display the welcome screen.

Step 5. Configure for Spark development

Set up VS Code for development on the DGX Spark platform.

## Launch VS Code if not already running
code

## Or create a new project directory and open it
mkdir ~/spark-dev-workspace
cd ~/spark-dev-workspace
code .

From within VS Code:

• Open File > Preferences > Settings
• Search for "terminal integrated shell" to configure default terminal
• Install recommended extensions via Extensions tab (left sidebar)

Step 6. Validate setup and test functionality

Test core VS Code functionality to ensure proper operation on ARM64.

Create a test file:

## Create test directory and file
mkdir ~/vscode-test
cd ~/vscode-test
echo 'print("Hello from DGX Spark!")' > test.py
code test.py

Within VS Code:

• Verify syntax highlighting works
• Open integrated terminal (Terminal > New Terminal)
• Run the test script: python3 test.py
• Test Git integration by running git status in the terminal

Step 7. Troubleshooting

Symptom	Cause	Fix
dpkg: dependency problems during install	Missing dependencies	Run sudo apt-get install -f
VS Code won't launch with GUI error	No display server/X11	Verify GUI desktop is running: echo $DISPLAY
Extensions fail to install	Network connectivity or ARM64 compatibility	Check internet connection, verify extension ARM64 support

Step 8. Uninstalling VS Code

Warning: Uninstalling VS Code will remove all user settings and extensions.

To remove VS Code if needed:

## Remove VS Code package
sudo apt-get remove code

## Remove configuration files (optional)
rm -rf ~/.config/Code
rm -rf ~/.vscode

Access with NVIDIA Sync

Step 1. Install and configure NVIDIA Sync

Follow the NVIDIA Sync setup guide to:

• Install NVIDIA Sync for your operating system
• Configure which development tools you want to use (VS Code, Cursor, Terminal, etc.)
• Add your DGX Spark device by providing its hostname/IP and credentials

NVIDIA Sync will automatically configure SSH key-based authentication for secure, password-free access.

Step 2. Launch VS Code through NVIDIA Sync

• Click the NVIDIA Sync icon in your system tray/taskbar
• Ensure your device is connected (click "Connect" if needed)
• Click on "VS Code" to launch it with an automatic SSH connection to your Spark
• Wait for the remote connection to be established (may ask your local machine for a password or to authorize the connection)
• It may prompt you to "trust the authors of the files in this folder" when you first land in the home directory after a successful SSH connection

Step 3. Validation and follow-ups

• Verify that you can access your Spark's filesystem with VS Code as a text editor
• Open the integrated terminal in VS Code and run test commands like hostnamectl and whoami to ensure you are remotely accessing your Spark
• Navigate to a specific file path or directory and start editing/writing files
• Install VS Code extensions for your development workflow (Python, Docker, GitLens, etc.)
• Clone repositories from GitHub or other version control systems
• Configure and locally host an LLM code assistant if desired